{
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   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Help on built-in function sqrt:\n\nsqrt(...)\n    sqrt(input, out=None) -> Tensor\n    \n    Returns a new tensor with the square-root of the elements of :attr:`input`.\n    \n    .. math::\n        \\text{out}_{i} = \\sqrt{\\text{input}_{i}}\n    \n    Args:\n        input (Tensor): the input tensor.\n        out (Tensor, optional): the output tensor.\n    \n    Example::\n    \n        >>> a = torch.randn(4)\n        >>> a\n        tensor([-2.0755,  1.0226,  0.0831,  0.4806])\n        >>> torch.sqrt(a)\n        tensor([    nan,  1.0112,  0.2883,  0.6933])\n\n"
    }
   ],
   "source": [
    "import torch\n",
    "help(torch.sqrt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "tensor([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "a = torch.Tensor([1,4,9,16,25,36,49,64,81,100])\n",
    "torch.sqrt(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}